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Engineering & product playbooks
Hands-on playbooks, decision frameworks, and case studies from the team building AI-native products at CodeNicely.
How to Retire a Legacy System Without Killing the Business
Replacing a business-critical legacy system isn't a code problem — it's a behavioral contract problem. Here's the playbook we use to retire 10-year-old systems while live traffic keeps flowing.
How GimBooks Kept AI Accurate Across 3M Downloads
When an AI bookkeeping feature works at 10K users but breaks at 500K, the instinct is to blame data volume. The GimBooks case study shows the real culprit is usually segment collapse — and the fix is architectural, not statistical.
Questions to Ask Before Hiring an AI SaaS Dev Partner
Most AI SaaS vendor pitches look identical until you ask the right questions. Here are the 15 a Series A founder should run through before signing — and the answers that separate operators from demo-builders.
Temporal Fusion vs. LSTM: Pick One for Demand Forecasting
Most TFT-vs-LSTM comparisons optimize for benchmark RMSE on clean data. Here's how the two architectures actually behave in production demand forecasting — covariates, retraining cadence, and serving cost at SKU scale.
Feature Stores Explained: Why Your AI Keeps Training on Lies
Your credit-scoring model passes every offline test, then degrades two weeks after deployment. The culprit isn't drift — it's that your training pipeline and your serving pipeline are computing features differently, and no one is enforcing they match.
AI Evaluation Metrics Cheatsheet: Pick the Right One
Most teams pick an AI evaluation metric because it was easy to instrument, then discover months later that the number looked fine while a key account churned. This cheatsheet maps the metrics to the business decisions they actually encode.
5 Mistakes Teams Make Shipping AI to an E-Pharmacy
Most e-pharmacy AI failures are not model accuracy problems. They are context, escalation, and output design problems that only surface once pharmacists and patients start ignoring the recommendations you spent six months building.
How to Monitor an AI Feature After It Ships
Your APM dashboard will tell you the inference endpoint is healthy right up until users start churning. Here's the playbook for catching AI feature degradation before it costs you revenue.
Synchronous vs. Async AI Pipelines: Pick the Right One
Your first AI feature shipped synchronously. Your second probably shouldn't. Here's the decision rule for picking between real-time and async ML pipelines — and why getting it wrong in healthcare is a compliance issue, not just a latency one.
Questions to Ask Before Hiring an AI Logistics Dev Partner
Every agency pitching freight-tech founders shows the same fleet logos and claims they've solved empty miles. Here are the 15 questions that separate vendors who've shipped marketplace AI from ones who've only run notebooks on clean datasets.
How KarroFin Scored 250K Users Without a Credit Bureau
KarroFin's underwriting model was rejecting creditworthy borrowers for the wrong reason: absence of bureau data. Here's the engineering call that fixed it, and why chasing the bureau score is the wrong target for any lender serving thin-file users.
Retrieval-Augmented Generation: What It Is and When It Breaks
Your competitor demoed an AI knowledge assistant and now the board wants one. Before you greenlight a RAG build, here is what it actually does, where it silently fails, and when fine-tuning or plain search beats it.